Effect of cutout on stochastic natural frequency of composite curved panels
Effect of cutout on stochastic natural frequency of composite curved panels
The present computational study investigates on stochastic natural frequency analyses of laminated composite curved panels with cutout based on support vector regression (SVR) model. The SVR based uncertainty quantification (UQ) algorithm in conjunction with Latin hypercube sampling is developed to achieve computational efficiency. The convergence of the present algorithm for laminated composite curved panels with cutout is validated with original finite element (FE) analysis along with traditional Monte Carlo simulation (MCS). The variations of input parameters (both individual and combined cases) are studied to portray their relative effect on the output quantity of interest. The performance of the SVR based uncertainty quantification is found to be satisfactory in the domain of input variables in dealing low and high dimensional spaces. The layer-wise variability of geometric and material properties are included considering the effect of twist angle, cutout sizes and geometries (such as cylindrical, spherical, hyperbolic paraboloid and plate). The sensitivities of input parameters in terms of coefficient of variation are enumerated to project the relative importance of different random inputs on natural frequencies. Subsequently, the noise induced effects on SVR based computational algorithm are presented to map the inevitable variability in practical field of applications.
Composite, Cutout, Noise, Random natural frequency, Support vector regression, Uncertainty quantification
188-202
Dey, S.
21680630-2d00-4958-9993-4c879c6be405
Mukhopadhyay, T.
2ae18ab0-7477-40ac-ae22-76face7be475
Sahu, S. K.
33b08260-3abf-46f5-adae-7275d1eea8be
Adhikari, S.
82960baf-916c-496e-aa85-fc7de09a1626
15 November 2016
Dey, S.
21680630-2d00-4958-9993-4c879c6be405
Mukhopadhyay, T.
2ae18ab0-7477-40ac-ae22-76face7be475
Sahu, S. K.
33b08260-3abf-46f5-adae-7275d1eea8be
Adhikari, S.
82960baf-916c-496e-aa85-fc7de09a1626
Dey, S., Mukhopadhyay, T., Sahu, S. K. and Adhikari, S.
(2016)
Effect of cutout on stochastic natural frequency of composite curved panels.
Composites Part B: Engineering, 105, .
(doi:10.1016/j.compositesb.2016.08.028).
Abstract
The present computational study investigates on stochastic natural frequency analyses of laminated composite curved panels with cutout based on support vector regression (SVR) model. The SVR based uncertainty quantification (UQ) algorithm in conjunction with Latin hypercube sampling is developed to achieve computational efficiency. The convergence of the present algorithm for laminated composite curved panels with cutout is validated with original finite element (FE) analysis along with traditional Monte Carlo simulation (MCS). The variations of input parameters (both individual and combined cases) are studied to portray their relative effect on the output quantity of interest. The performance of the SVR based uncertainty quantification is found to be satisfactory in the domain of input variables in dealing low and high dimensional spaces. The layer-wise variability of geometric and material properties are included considering the effect of twist angle, cutout sizes and geometries (such as cylindrical, spherical, hyperbolic paraboloid and plate). The sensitivities of input parameters in terms of coefficient of variation are enumerated to project the relative importance of different random inputs on natural frequencies. Subsequently, the noise induced effects on SVR based computational algorithm are presented to map the inevitable variability in practical field of applications.
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Published date: 15 November 2016
Additional Information:
Funding Information:
TM acknowledges the financial support from Swansea University through the award of Zienkiewicz Scholarship during the period of this work. SA acknowledges the financial support from The Royal Society of London through the Wolfson Research Merit award.
Publisher Copyright:
© 2016 Elsevier Ltd
Keywords:
Composite, Cutout, Noise, Random natural frequency, Support vector regression, Uncertainty quantification
Identifiers
Local EPrints ID: 483535
URI: http://eprints.soton.ac.uk/id/eprint/483535
ISSN: 1359-8368
PURE UUID: 45232a0c-82d3-4980-9e3a-9192337caf7b
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Date deposited: 01 Nov 2023 17:56
Last modified: 18 Mar 2024 04:10
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Contributors
Author:
S. Dey
Author:
T. Mukhopadhyay
Author:
S. K. Sahu
Author:
S. Adhikari
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